code: def _run(self, data, axes=None, keepdims=None):  # type: ignore
      axes = tuple(axes) if axes is not None else None
      if data.size == 0:
          maxvalue = (
              np.iinfo(data.dtype).max
              if np.issubdtype(data.dtype, np.integer)
              else np.inf
          )
          return self.reduce_constant(data, maxvalue, axes, keepdims)
  
      res = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)
      if keepdims == 0 and not isinstance(res, np.ndarray):
          # The runtime must return a numpy array of a single float.
          res = np.array(res)
      return (res,)
  
file_path: onnx/onnx/reference/ops/op_reduce_min.py
imports: import numpy
  from onnx.reference.ops._op import OpRunReduceNumpy
label_desc: Use image node_id 1 for calling the ReduceMin_1 obj's underlying member method code with example usage: obj._run(data, axes, keepdims) and returns: res, self